Overview

Dataset statistics

Number of variables12
Number of observations1225
Missing cells0
Missing cells (%)0.0%
Duplicate rows58
Duplicate rows (%)4.7%
Total size in memory115.0 KiB
Average record size in memory96.1 B

Variable types

Numeric12

Alerts

Dataset has 58 (4.7%) duplicate rowsDuplicates
residual sugar is highly overall correlated with densityHigh correlation
chlorides is highly overall correlated with density and 1 other fieldsHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly overall correlated with free sulfur dioxide and 1 other fieldsHigh correlation
density is highly overall correlated with residual sugar and 3 other fieldsHigh correlation
alcohol is highly overall correlated with chlorides and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-04-14 16:34:58.634144
Analysis finished2023-04-14 16:35:06.418907
Duration7.78 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ)

Distinct53
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8230612
Minimum3.9
Maximum14.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-04-14T12:35:06.460769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3.9
5-th percentile5.6
Q16.3
median6.8
Q37.3
95-th percentile8.28
Maximum14.2
Range10.3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.83440178
Coefficient of variation (CV)0.12229141
Kurtosis5.1553154
Mean6.8230612
Median Absolute Deviation (MAD)0.5
Skewness0.82337227
Sum8358.25
Variance0.69622632
MonotonicityNot monotonic
2023-04-14T12:35:06.523093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.8 81
 
6.6%
6.4 73
 
6.0%
6.6 63
 
5.1%
6.2 62
 
5.1%
6.9 61
 
5.0%
7 61
 
5.0%
6.7 54
 
4.4%
6 54
 
4.4%
7.4 53
 
4.3%
6.5 52
 
4.2%
Other values (43) 611
49.9%
ValueCountFrequency (%)
3.9 1
 
0.1%
4.2 1
 
0.1%
4.4 1
 
0.1%
4.7 2
 
0.2%
4.8 1
 
0.1%
4.9 2
 
0.2%
5 9
0.7%
5.1 6
0.5%
5.2 6
0.5%
5.3 8
0.7%
ValueCountFrequency (%)
14.2 1
 
0.1%
9.8 1
 
0.1%
9.5 1
 
0.1%
9.4 3
0.2%
9.2 7
0.6%
9.1 2
 
0.2%
8.9 4
0.3%
8.8 5
0.4%
8.7 5
0.4%
8.6 7
0.6%

volatile acidity
Real number (ℝ)

Distinct86
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27543265
Minimum0.08
Maximum0.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-04-14T12:35:06.580949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.15
Q10.21
median0.26
Q30.32
95-th percentile0.46
Maximum0.76
Range0.68
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.098466886
Coefficient of variation (CV)0.35749896
Kurtosis3.0701209
Mean0.27543265
Median Absolute Deviation (MAD)0.05
Skewness1.3748233
Sum337.405
Variance0.0096957277
MonotonicityNot monotonic
2023-04-14T12:35:06.634301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.25 76
 
6.2%
0.27 64
 
5.2%
0.23 62
 
5.1%
0.28 62
 
5.1%
0.24 61
 
5.0%
0.21 59
 
4.8%
0.26 59
 
4.8%
0.3 56
 
4.6%
0.22 51
 
4.2%
0.2 49
 
4.0%
Other values (76) 626
51.1%
ValueCountFrequency (%)
0.08 1
 
0.1%
0.09 1
 
0.1%
0.1 4
 
0.3%
0.105 1
 
0.1%
0.11 1
 
0.1%
0.115 1
 
0.1%
0.12 14
1.1%
0.13 11
0.9%
0.14 11
0.9%
0.15 22
1.8%
ValueCountFrequency (%)
0.76 1
 
0.1%
0.75 1
 
0.1%
0.74 1
 
0.1%
0.695 1
 
0.1%
0.69 1
 
0.1%
0.68 1
 
0.1%
0.67 2
0.2%
0.66 2
0.2%
0.64 3
0.2%
0.615 1
 
0.1%

citric acid
Real number (ℝ)

Distinct79
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33714286
Minimum0
Maximum0.99
Zeros4
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-04-14T12:35:06.693116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.172
Q10.27
median0.32
Q30.39
95-th percentile0.568
Maximum0.99
Range0.99
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.12366476
Coefficient of variation (CV)0.36680227
Kurtosis2.7200544
Mean0.33714286
Median Absolute Deviation (MAD)0.06
Skewness0.99769706
Sum413
Variance0.015292974
MonotonicityNot monotonic
2023-04-14T12:35:06.829707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 82
 
6.7%
0.29 66
 
5.4%
0.28 64
 
5.2%
0.32 62
 
5.1%
0.49 60
 
4.9%
0.26 59
 
4.8%
0.34 57
 
4.7%
0.27 53
 
4.3%
0.36 50
 
4.1%
0.24 50
 
4.1%
Other values (69) 622
50.8%
ValueCountFrequency (%)
0 4
0.3%
0.01 1
 
0.1%
0.02 1
 
0.1%
0.03 2
 
0.2%
0.04 3
0.2%
0.05 3
0.2%
0.06 3
0.2%
0.07 2
 
0.2%
0.08 1
 
0.1%
0.09 5
0.4%
ValueCountFrequency (%)
0.99 1
 
0.1%
0.91 1
 
0.1%
0.86 1
 
0.1%
0.82 1
 
0.1%
0.81 1
 
0.1%
0.8 1
 
0.1%
0.74 16
1.3%
0.73 1
 
0.1%
0.71 3
 
0.2%
0.7 1
 
0.1%

residual sugar
Real number (ℝ)

Distinct221
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.252
Minimum0.6
Maximum26.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-04-14T12:35:06.885517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.1
Q11.7
median5.1
Q39.8
95-th percentile15.48
Maximum26.05
Range25.45
Interquartile range (IQR)8.1

Descriptive statistics

Standard deviation4.8621628
Coefficient of variation (CV)0.77769719
Kurtosis-0.26359641
Mean6.252
Median Absolute Deviation (MAD)3.5
Skewness0.78586003
Sum7658.7
Variance23.640627
MonotonicityNot monotonic
2023-04-14T12:35:06.940857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 46
 
3.8%
1.4 42
 
3.4%
1.5 42
 
3.4%
1.6 36
 
2.9%
1.3 32
 
2.6%
1.1 32
 
2.6%
1 29
 
2.4%
1.7 24
 
2.0%
1.8 23
 
1.9%
2.2 19
 
1.6%
Other values (211) 900
73.5%
ValueCountFrequency (%)
0.6 1
 
0.1%
0.7 2
 
0.2%
0.8 9
 
0.7%
0.9 9
 
0.7%
1 29
2.4%
1.05 1
 
0.1%
1.1 32
2.6%
1.15 1
 
0.1%
1.2 46
3.8%
1.25 1
 
0.1%
ValueCountFrequency (%)
26.05 1
 
0.1%
22.6 1
 
0.1%
22 1
 
0.1%
19.8 1
 
0.1%
19.5 1
 
0.1%
19.4 1
 
0.1%
19.3 1
 
0.1%
19.25 1
 
0.1%
18.95 3
0.2%
18.8 1
 
0.1%

chlorides
Real number (ℝ)

Distinct99
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.046306939
Minimum0.014
Maximum0.271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-04-14T12:35:06.994693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.014
5-th percentile0.028
Q10.036
median0.042
Q30.05
95-th percentile0.069
Maximum0.271
Range0.257
Interquartile range (IQR)0.014

Descriptive statistics

Standard deviation0.023736031
Coefficient of variation (CV)0.51258044
Kurtosis29.172272
Mean0.046306939
Median Absolute Deviation (MAD)0.007
Skewness4.7007972
Sum56.726
Variance0.00056339918
MonotonicityNot monotonic
2023-04-14T12:35:07.050024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.036 62
 
5.1%
0.038 53
 
4.3%
0.042 49
 
4.0%
0.04 49
 
4.0%
0.05 45
 
3.7%
0.039 44
 
3.6%
0.043 44
 
3.6%
0.047 42
 
3.4%
0.045 41
 
3.3%
0.049 40
 
3.3%
Other values (89) 756
61.7%
ValueCountFrequency (%)
0.014 1
 
0.1%
0.015 1
 
0.1%
0.017 1
 
0.1%
0.018 1
 
0.1%
0.019 4
0.3%
0.02 5
0.4%
0.021 4
0.3%
0.022 3
 
0.2%
0.023 5
0.4%
0.024 8
0.7%
ValueCountFrequency (%)
0.271 1
0.1%
0.244 1
0.1%
0.24 1
0.1%
0.217 1
0.1%
0.211 1
0.1%
0.208 1
0.1%
0.201 1
0.1%
0.197 2
0.2%
0.185 1
0.1%
0.174 2
0.2%

free sulfur dioxide
Real number (ℝ)

Distinct93
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.683265
Minimum2
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-04-14T12:35:07.105963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile12
Q124
median34
Q346
95-th percentile64
Maximum289
Range287
Interquartile range (IQR)22

Descriptive statistics

Standard deviation17.578886
Coefficient of variation (CV)0.49263671
Kurtosis35.246182
Mean35.683265
Median Absolute Deviation (MAD)11
Skewness2.8993195
Sum43712
Variance309.01724
MonotonicityNot monotonic
2023-04-14T12:35:07.158635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 40
 
3.3%
31 38
 
3.1%
36 37
 
3.0%
29 34
 
2.8%
34 33
 
2.7%
37 32
 
2.6%
26 32
 
2.6%
32 31
 
2.5%
28 30
 
2.4%
33 30
 
2.4%
Other values (83) 888
72.5%
ValueCountFrequency (%)
2 1
 
0.1%
4 2
 
0.2%
5 4
 
0.3%
6 10
0.8%
7 3
 
0.2%
8 6
 
0.5%
9 8
0.7%
10 11
0.9%
11 12
1.0%
12 16
1.3%
ValueCountFrequency (%)
289 1
0.1%
138.5 1
0.1%
101 1
0.1%
86 1
0.1%
85 2
0.2%
82.5 1
0.1%
82 1
0.1%
78 2
0.2%
77 1
0.1%
76 2
0.2%

total sulfur dioxide
Real number (ℝ)

Distinct206
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.5302
Minimum9
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-04-14T12:35:07.215955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile76
Q1107
median132
Q3168
95-th percentile215
Maximum440
Range431
Interquartile range (IQR)61

Descriptive statistics

Standard deviation43.897919
Coefficient of variation (CV)0.31688338
Kurtosis2.3890729
Mean138.5302
Median Absolute Deviation (MAD)29
Skewness0.78709566
Sum169699.5
Variance1927.0273
MonotonicityNot monotonic
2023-04-14T12:35:07.269778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122 20
 
1.6%
111 20
 
1.6%
114 19
 
1.6%
116 17
 
1.4%
115 17
 
1.4%
168 17
 
1.4%
124 17
 
1.4%
107 16
 
1.3%
128 16
 
1.3%
152 15
 
1.2%
Other values (196) 1051
85.8%
ValueCountFrequency (%)
9 1
0.1%
24 1
0.1%
29 1
0.1%
30 1
0.1%
34 1
0.1%
37 1
0.1%
41 1
0.1%
47 1
0.1%
49 1
0.1%
50 1
0.1%
ValueCountFrequency (%)
440 1
0.1%
366.5 1
0.1%
344 1
0.1%
303 1
0.1%
282 1
0.1%
272 2
0.2%
260 1
0.1%
255 1
0.1%
253 1
0.1%
251 1
0.1%

density
Real number (ℝ)

Distinct492
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99391241
Minimum0.98722
Maximum1.00295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-04-14T12:35:07.324122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.98722
5-th percentile0.989614
Q10.99166
median0.9937
Q30.99604
95-th percentile0.9986
Maximum1.00295
Range0.01573
Interquartile range (IQR)0.00438

Descriptive statistics

Standard deviation0.0028789526
Coefficient of variation (CV)0.0028965858
Kurtosis-0.730047
Mean0.99391241
Median Absolute Deviation (MAD)0.00217
Skewness0.24350129
Sum1217.5427
Variance8.2883681 × 10-6
MonotonicityNot monotonic
2023-04-14T12:35:07.378993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.992 22
 
1.8%
0.993 17
 
1.4%
0.994 15
 
1.2%
0.9928 14
 
1.1%
0.9972 14
 
1.1%
0.9932 14
 
1.1%
0.9918 13
 
1.1%
0.9942 13
 
1.1%
0.9914 13
 
1.1%
0.9966 13
 
1.1%
Other values (482) 1077
87.9%
ValueCountFrequency (%)
0.98722 1
0.1%
0.98742 1
0.1%
0.98746 2
0.2%
0.98802 1
0.1%
0.98823 1
0.1%
0.98836 1
0.1%
0.98845 1
0.1%
0.98856 1
0.1%
0.98862 1
0.1%
0.98867 1
0.1%
ValueCountFrequency (%)
1.00295 1
0.1%
1.001 1
0.1%
1.00098 1
0.1%
1.0008 1
0.1%
1.0006 1
0.1%
1.0005 1
0.1%
1.00047 1
0.1%
1.0004 2
0.2%
1.00037 1
0.1%
1.0003 1
0.1%

pH
Real number (ℝ)

Distinct86
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1849796
Minimum2.8
Maximum3.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-04-14T12:35:07.438320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile2.96
Q13.08
median3.17
Q33.27
95-th percentile3.47
Maximum3.81
Range1.01
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.15519023
Coefficient of variation (CV)0.048725659
Kurtosis0.4218719
Mean3.1849796
Median Absolute Deviation (MAD)0.1
Skewness0.4993355
Sum3901.6
Variance0.024084007
MonotonicityNot monotonic
2023-04-14T12:35:07.498237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.22 44
 
3.6%
3.14 40
 
3.3%
3.11 38
 
3.1%
3.25 38
 
3.1%
3.18 37
 
3.0%
3.19 35
 
2.9%
3.16 34
 
2.8%
3.12 33
 
2.7%
3.08 32
 
2.6%
3.2 32
 
2.6%
Other values (76) 862
70.4%
ValueCountFrequency (%)
2.8 1
 
0.1%
2.83 2
 
0.2%
2.85 6
0.5%
2.86 2
 
0.2%
2.87 3
 
0.2%
2.88 3
 
0.2%
2.89 4
 
0.3%
2.9 10
0.8%
2.91 5
0.4%
2.92 4
 
0.3%
ValueCountFrequency (%)
3.81 1
0.1%
3.8 1
0.1%
3.75 1
0.1%
3.69 1
0.1%
3.68 1
0.1%
3.66 1
0.1%
3.65 1
0.1%
3.63 2
0.2%
3.62 1
0.1%
3.6 2
0.2%

sulphates
Real number (ℝ)

Distinct68
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49075918
Minimum0.25
Maximum1.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-04-14T12:35:07.555801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile0.33
Q10.41
median0.48
Q30.55
95-th percentile0.7
Maximum1.08
Range0.83
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.11580633
Coefficient of variation (CV)0.23597384
Kurtosis2.1365064
Mean0.49075918
Median Absolute Deviation (MAD)0.07
Skewness1.0333753
Sum601.18
Variance0.013411106
MonotonicityNot monotonic
2023-04-14T12:35:07.609256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.44 60
 
4.9%
0.46 58
 
4.7%
0.38 57
 
4.7%
0.48 56
 
4.6%
0.5 56
 
4.6%
0.42 47
 
3.8%
0.52 47
 
3.8%
0.51 44
 
3.6%
0.47 43
 
3.5%
0.45 43
 
3.5%
Other values (58) 714
58.3%
ValueCountFrequency (%)
0.25 3
 
0.2%
0.26 1
 
0.1%
0.27 3
 
0.2%
0.28 3
 
0.2%
0.29 7
0.6%
0.3 13
1.1%
0.31 11
0.9%
0.32 17
1.4%
0.33 16
1.3%
0.34 12
1.0%
ValueCountFrequency (%)
1.08 1
 
0.1%
1.01 1
 
0.1%
0.99 1
 
0.1%
0.98 3
0.2%
0.97 1
 
0.1%
0.96 1
 
0.1%
0.88 3
0.2%
0.87 2
0.2%
0.86 1
 
0.1%
0.83 2
0.2%

alcohol
Real number (ℝ)

Distinct75
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.526264
Minimum8
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-04-14T12:35:07.741847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8.9
Q19.5
median10.4
Q311.4
95-th percentile12.7
Maximum14
Range6
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation1.2316939
Coefficient of variation (CV)0.11701149
Kurtosis-0.75921505
Mean10.526264
Median Absolute Deviation (MAD)1
Skewness0.45499016
Sum12894.673
Variance1.5170698
MonotonicityNot monotonic
2023-04-14T12:35:07.794789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 66
 
5.4%
9.4 55
 
4.5%
9.2 50
 
4.1%
9.3 45
 
3.7%
11 43
 
3.5%
10.8 41
 
3.3%
9 41
 
3.3%
10.4 40
 
3.3%
10.5 39
 
3.2%
9.1 35
 
2.9%
Other values (65) 770
62.9%
ValueCountFrequency (%)
8 1
 
0.1%
8.4 1
 
0.1%
8.5 1
 
0.1%
8.6 6
 
0.5%
8.7 18
 
1.5%
8.8 24
2.0%
8.9 27
2.2%
9 41
3.3%
9.1 35
2.9%
9.2 50
4.1%
ValueCountFrequency (%)
14 2
 
0.2%
13.9 1
 
0.1%
13.6 3
 
0.2%
13.5 3
 
0.2%
13.4 5
0.4%
13.3 3
 
0.2%
13.2 3
 
0.2%
13.1 3
 
0.2%
13.05 1
 
0.1%
13 10
0.8%

quality
Real number (ℝ)

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9053061
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-04-14T12:35:07.840104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum8
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.88146098
Coefficient of variation (CV)0.14926592
Kurtosis0.16805373
Mean5.9053061
Median Absolute Deviation (MAD)1
Skewness0.063574244
Sum7234
Variance0.77697346
MonotonicityNot monotonic
2023-04-14T12:35:07.876981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 544
44.4%
5 358
29.2%
7 241
19.7%
8 43
 
3.5%
4 32
 
2.6%
3 7
 
0.6%
ValueCountFrequency (%)
3 7
 
0.6%
4 32
 
2.6%
5 358
29.2%
6 544
44.4%
7 241
19.7%
8 43
 
3.5%
ValueCountFrequency (%)
8 43
 
3.5%
7 241
19.7%
6 544
44.4%
5 358
29.2%
4 32
 
2.6%
3 7
 
0.6%

Interactions

2023-04-14T12:35:05.709231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:58.729609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:59.393146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:59.998794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:00.679088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:01.267928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:01.939523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:02.541632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:03.198545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:03.819092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:04.477914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:05.041130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:05.757074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:58.778454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:59.443980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:00.049625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:00.727509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:01.316311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:01.990361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:02.589481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:03.249383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:03.866932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:04.525284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:05.089964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:05.805426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:58.905350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:59.494838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:00.100970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:00.778355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:01.366139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:02.042706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:02.639292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:03.303715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:03.917296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:04.572123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:05.138894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:05.854263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:58.953186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:59.544671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:00.149806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:00.828753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:01.414505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:02.092546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:02.687141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:03.355542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:03.965138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:04.620492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:05.190729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:05.901108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:59.002803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:59.596030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:00.281040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:00.876605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:01.464391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:02.142903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:02.734503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:03.405903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:04.014500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:04.667335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:05.240086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:05.947602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:59.051642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:59.644867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:00.328404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:00.924975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:01.510765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:02.189754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:02.783342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:03.456733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:04.060342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:04.712696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:05.286925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:05.997442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:59.102036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:59.697221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:00.379236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:00.973808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:01.640870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:02.241089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:02.833706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:03.509086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:04.108810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:04.761532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:05.337342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:06.044855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:59.148909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:59.745060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:00.428592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:01.022175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:01.689711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:02.289933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:02.879565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:03.558922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:04.156651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:04.806957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:05.386174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:06.097692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:59.201262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:59.799408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:00.481690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:01.074999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:01.743049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:02.343257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:03.010175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:03.612263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:04.208994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:04.857790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:05.437524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:06.144096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:59.249116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:59.850255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:00.532042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:01.124363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:01.792885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:02.393093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:03.058022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:03.663093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:04.335855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:04.904328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:05.486361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:06.189945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:59.296205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:59.896612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:00.578895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:01.169724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:01.838254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:02.442438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:03.103336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:03.712454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:04.382699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:04.948186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:05.533757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:06.238394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:59.345042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:34:59.949436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:00.628250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:01.219092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:01.889093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:02.491279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:03.151176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:03.769265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:04.431059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:04.995035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T12:35:05.661860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-04-14T12:35:07.918966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
fixed acidity1.000-0.0490.2620.1300.116-0.0200.1060.293-0.435-0.032-0.149-0.079
volatile acidity-0.0491.000-0.2010.1220.007-0.0880.092-0.004-0.0770.0090.040-0.174
citric acid0.262-0.2011.0000.0300.0260.0810.0680.102-0.1530.073-0.0560.053
residual sugar0.1300.1220.0301.0000.2230.3330.4480.781-0.1930.022-0.439-0.076
chlorides0.1160.0070.0260.2231.0000.1510.4010.524-0.0670.119-0.589-0.337
free sulfur dioxide-0.020-0.0880.0810.3330.1511.0000.6100.312-0.0210.091-0.262-0.007
total sulfur dioxide0.1060.0920.0680.4480.4010.6101.0000.566-0.0250.174-0.483-0.206
density0.293-0.0040.1020.7810.5240.3120.5661.000-0.1150.107-0.823-0.353
pH-0.435-0.077-0.153-0.193-0.067-0.021-0.025-0.1151.0000.1520.1540.083
sulphates-0.0320.0090.0730.0220.1190.0910.1740.1070.1521.000-0.0490.016
alcohol-0.1490.040-0.056-0.439-0.589-0.262-0.483-0.8230.154-0.0491.0000.445
quality-0.079-0.1740.053-0.076-0.337-0.007-0.206-0.3530.0830.0160.4451.000

Missing values

2023-04-14T12:35:06.306757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-14T12:35:06.382501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
06.00.290.4110.800.04855.0149.00.993703.090.5910.9666677
15.40.530.162.700.03634.0128.00.988563.200.5313.2000008
27.10.250.392.100.03630.0124.00.990803.280.4312.2000008
37.30.280.351.600.05431.0148.00.991783.180.4710.7000005
46.50.320.345.700.04427.091.00.991843.280.6012.0000007
56.30.300.918.200.03450.0199.00.993943.390.4911.7000006
67.00.360.1411.600.04335.0228.00.997703.130.518.9000005
77.60.260.361.600.0326.0106.00.993003.150.4010.4000004
88.30.180.301.100.03320.057.00.991093.020.5111.0000006
98.70.310.7314.350.04427.0191.01.000132.960.888.7000005
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
12156.400.410.016.100.04820.070.00.993623.190.4210.05
12166.300.240.352.300.03943.0109.00.990563.340.4411.86
12175.800.540.001.400.03340.0107.00.989183.260.3512.45
12189.400.280.301.600.04536.0139.00.995343.110.499.35
12196.500.210.421.100.05933.0101.00.992703.120.389.76
12206.150.210.373.200.02120.080.00.990763.390.4712.05
12217.100.280.441.800.03232.0107.00.990703.250.4812.27
12225.900.130.281.900.05020.078.00.991803.430.6410.86
12236.800.300.296.200.02529.095.00.990713.030.3212.97
12247.400.250.362.050.05031.0100.00.992003.190.4410.86

Duplicate rows

Most frequently occurring

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality# duplicates
206.80.180.3012.80.06219.0171.00.998083.000.529.074
427.40.190.3012.80.05348.5229.00.998603.140.499.174
146.40.240.495.80.05325.0120.00.994203.010.9810.563
166.50.180.4114.20.03947.0129.00.996783.280.7210.373
186.60.300.258.00.03621.0124.00.993623.060.3810.863
216.80.190.7117.50.04221.0114.00.997842.850.509.563
357.20.230.3914.20.05849.0192.00.997902.980.489.073
417.40.160.3013.70.05633.0168.00.998252.900.448.773
05.00.270.324.50.03258.0178.00.989563.450.3112.672
15.50.120.331.00.03823.0131.00.991643.250.459.852